Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems. Willard, J., Jia, X., Xu, S., Steinbach, M., & Kumar, V. ACM Computing Surveys, 55(4):1–37, November, 2022. ![Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.
@article{willard_integrating_2022,
title = {Integrating {Scientific} {Knowledge} with {Machine} {Learning} for {Engineering} and {Environmental} {Systems}},
volume = {55},
issn = {0360-0300, 1557-7341},
url = {https://dl.acm.org/doi/10.1145/3514228},
doi = {10.1145/3514228},
abstract = {There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.},
language = {en},
number = {4},
urldate = {2023-04-13},
journal = {ACM Computing Surveys},
author = {Willard, Jared and Jia, Xiaowei and Xu, Shaoming and Steinbach, Michael and Kumar, Vipin},
month = nov,
year = {2022},
pages = {1--37},
}
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